Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81784
Title: Pretreatment prediction of adaptive radiation therapy eligibility using MRI-based radiomics for advanced nasopharyngeal carcinoma patients
Authors: Yu, TT 
Lam, SK 
To, LH 
Tse, KY 
Cheng, NY 
Fan, YN 
Lo, CL 
Or, KW 
Chan, ML 
Hui, KC 
Chan, FC 
Hui, WM 
Ngai, LK 
Lee, FKH
Au, KH
Yip, CWY
Zhang, Y
Cai, J 
Keywords: Radiomics
Nasopharyngeal carcinoma
Adaptive radiation therapy
Tumor shrinkage
Magnetic resonance imaging
Issue Date: 2019
Publisher: Frontiers Research Foundation
Source: Frontiers in oncology, 16 Oct. 2019, v. 9, 1050, p. 1-10 How to cite?
Journal: Frontiers in oncology 
Abstract: Background and purpose: Adaptive radiotherapy (ART) can compensate for the dosimetric impacts induced by anatomic and geometric variations in patients with nasopharyngeal carcinoma (NPC); Yet, the need for ART can only be assessed during the radiation treatment and the implementation of ART is resource intensive. Therefore, we aimed to determine tumoral biomarkers using pre-treatment MR images for predicting ART eligibility in NPC patients prior to the start of treatment.
Methods: Seventy patients with biopsy-proven NPC (Stage II-IVB) in 2015 were enrolled into this retrospective study. Pre-treatment contrast-enhanced T1-w (CET1-w), T2-w MR images were processed and filtered using Laplacian of Gaussian (LoG) filter before radiomic features extraction. A total of 479 radiomics features, including the first-order (n = 90), shape (n = 14), and texture features (n = 375), were initially extracted from Gross-Tumor-Volume of primary tumor (GTVnp) using CET1-w, T2-w MR images. Patients were randomly divided into a training set (n = 51) and testing set (n = 19). The least absolute shrinkage and selection operator (LASSO) logistic regression model was applied for radiomic model construction in training set to select the most predictive features to predict patients who were replanned and assessed in the testing set. A double cross-validation approach of 100 resampled iterations with 3-fold nested cross-validation was employed in LASSO during model construction. The predictive performance of each model was evaluated using the area under the receiver operator characteristic (ROC) curve (AUC).
Results: In the present cohort, 13 of 70 patients (18.6%) underwent ART. Average AUCs in training and testing sets were 0.962 (95%CI: 0.961-0.963) and 0.852 (95%CI: 0.847-0.857) with 8 selected features for CET1-w model; 0.895 (95%CI: 0.893-0.896) and 0.750 (95%CI: 0.745-0.755) with 6 selected features for T2-w model; and 0.984 (95%CI: 0.983-0.984) and 0.930 (95%CI: 0.928-0.933) with 6 selected features for joint T1-T2 model, respectively. In general, the joint T1-T2 model outperformed either CET1-w or T2-w model alone.
Conclusions: Our study successfully showed promising capability of MRI-based radiomics features for pre-treatment identification of ART eligibility in NPC patients.
URI: http://hdl.handle.net/10397/81784
EISSN: 2234-943X
DOI: 10.3389/fonc.2019.01050
Rights: Copyright © 2019 Yu, Lam, To, Tse, Cheng, Fan, Lo, Or, Chan, Hui, Chan, Hui, Ngai, Lee, Au, Yip, Zhang and Cai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
The following publication Yu T, Lam S, To L, Tse K, Cheng N, Fan Y, Lo C, Or K, Chan M, Hui K, Chan F, Hui W, Ngai L, Lee FK, Au K, Yip CW, Zhang Y and Cai J (2019) Pretreatment Prediction of Adaptive Radiation Therapy Eligibility Using MRI-Based Radiomics for Advanced Nasopharyngeal Carcinoma Patients. Front. Oncol. 9:1050, 1-10 is available at https://dx.doi.org/10.3389/fonc.2019.01050
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